From c13dba86b2dbe581353b72602d7fa6e40991964c Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期三, 27 九月 2017 04:11:23 +0800
Subject: [PATCH] next
---
code/datasets.py | 533 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++-
1 files changed, 516 insertions(+), 17 deletions(-)
diff --git a/code/datasets.py b/code/datasets.py
index 4cd8449..f5941ae 100644
--- a/code/datasets.py
+++ b/code/datasets.py
@@ -3,15 +3,16 @@
import cv2
from torch.utils.data.dataset import Dataset
import os
-from PIL import Image
+from PIL import Image, ImageFilter
import utils
+from torchvision import transforms
def stack_grayscale_tensor(tensor):
tensor = torch.cat([tensor, tensor, tensor], 0)
return tensor
-class 300W_LP(Dataset):
+class Pose_300W_LP(Dataset):
def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.mat', image_mode='RGB'):
self.data_dir = data_dir
self.transform = transform
@@ -38,11 +39,13 @@
x_max = max(pt2d[0,:])
y_max = max(pt2d[1,:])
- k = 0.15
- x_min -= k * abs(x_max - x_min)
- y_min -= 4 * k * abs(y_max - y_min)
- x_max += k * abs(x_max - x_min)
- y_max += 0.4 * k * abs(y_max - y_min)
+ # k = 0.35 was being used beforehand
+ # k = 0.2 to 0.40
+ k = np.random.random_sample() * 0.2 + 0.2
+ x_min -= 0.6 * k * abs(x_max - x_min)
+ y_min -= 2 * k * abs(y_max - y_min)
+ x_max += 0.6 * k * abs(x_max - x_min)
+ y_max += 0.6 * k * abs(y_max - y_min)
img = img.crop((int(x_min), int(y_min), int(x_max), int(y_max)))
# We get the pose in radians
@@ -51,6 +54,19 @@
pitch = pose[0] * 180 / np.pi
yaw = pose[1] * 180 / np.pi
roll = pose[2] * 180 / np.pi
+
+ # Flip?
+ rnd = np.random.random_sample()
+ if rnd < 0.5:
+ yaw = -yaw
+ roll = -roll
+ img = img.transpose(Image.FLIP_LEFT_RIGHT)
+
+ # Blur?
+ rnd = np.random.random_sample()
+ if rnd < 0.05:
+ img = img.filter(ImageFilter.BLUR)
+
# Bin values
bins = np.array(range(-99, 102, 3))
binned_pose = np.digitize([yaw, pitch, roll], bins) - 1
@@ -59,11 +75,174 @@
shape = np.load(shape_path)
labels = torch.LongTensor(np.concatenate((binned_pose, shape), axis = 0))
+ cont_labels = torch.FloatTensor([yaw, pitch, roll])
if self.transform is not None:
img = self.transform(img)
- return img, labels, self.X_train[index]
+ return img, labels, cont_labels, self.X_train[index]
+
+ def __len__(self):
+ # 122,450
+ return self.length
+
+class Pose_300W_LP_random_ds(Dataset):
+ def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.mat', image_mode='RGB'):
+ self.data_dir = data_dir
+ self.transform = transform
+ self.img_ext = img_ext
+ self.annot_ext = annot_ext
+
+ filename_list = get_list_from_filenames(filename_path)
+
+ self.X_train = filename_list
+ self.y_train = filename_list
+ self.image_mode = image_mode
+ self.length = len(filename_list)
+
+ def __getitem__(self, index):
+ img = Image.open(os.path.join(self.data_dir, self.X_train[index] + self.img_ext))
+ img = img.convert(self.image_mode)
+ mat_path = os.path.join(self.data_dir, self.y_train[index] + self.annot_ext)
+ shape_path = os.path.join(self.data_dir, self.y_train[index] + '_shape.npy')
+
+ # Crop the face
+ pt2d = utils.get_pt2d_from_mat(mat_path)
+ x_min = min(pt2d[0,:])
+ y_min = min(pt2d[1,:])
+ x_max = max(pt2d[0,:])
+ y_max = max(pt2d[1,:])
+
+ # k = 0.2 to 0.40
+ k = np.random.random_sample() * 0.2 + 0.2
+ x_min -= 0.6 * k * abs(x_max - x_min)
+ y_min -= 2 * k * abs(y_max - y_min)
+ x_max += 0.6 * k * abs(x_max - x_min)
+ y_max += 0.6 * k * abs(y_max - y_min)
+ img = img.crop((int(x_min), int(y_min), int(x_max), int(y_max)))
+
+ # We get the pose in radians
+ pose = utils.get_ypr_from_mat(mat_path)
+ # And convert to degrees.
+ pitch = pose[0] * 180 / np.pi
+ yaw = pose[1] * 180 / np.pi
+ roll = pose[2] * 180 / np.pi
+
+ rnd = np.random.random_sample()
+ if rnd < 0.5:
+ ds = 10
+ original_size = img.size
+ img = img.resize((img.size[0] / ds, img.size[1] / ds), resample=Image.NEAREST)
+ img = img.resize((original_size[0], original_size[1]), resample=Image.NEAREST)
+
+ # Flip?
+ rnd = np.random.random_sample()
+ if rnd < 0.5:
+ yaw = -yaw
+ roll = -roll
+ img = img.transpose(Image.FLIP_LEFT_RIGHT)
+
+ # Blur?
+ rnd = np.random.random_sample()
+ if rnd < 0.05:
+ img = img.filter(ImageFilter.BLUR)
+
+ # Bin values
+ bins = np.array(range(-99, 102, 3))
+ binned_pose = np.digitize([yaw, pitch, roll], bins) - 1
+
+ # Get shape
+ shape = np.load(shape_path)
+
+ labels = torch.LongTensor(np.concatenate((binned_pose, shape), axis = 0))
+ cont_labels = torch.FloatTensor([yaw, pitch, roll])
+
+ if self.transform is not None:
+ img = self.transform(img)
+
+ return img, labels, cont_labels, self.X_train[index]
+
+ def __len__(self):
+ # 122,450
+ return self.length
+
+class Pose_300W_LP_SR(Dataset):
+ def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.mat', image_mode='RGB'):
+ self.data_dir = data_dir
+ self.transform = transform
+ self.img_ext = img_ext
+ self.annot_ext = annot_ext
+
+ filename_list = get_list_from_filenames(filename_path)
+
+ self.X_train = filename_list
+ self.y_train = filename_list
+ self.image_mode = image_mode
+ self.length = len(filename_list)
+
+ def __getitem__(self, index):
+ img = Image.open(os.path.join(self.data_dir, self.X_train[index] + self.img_ext))
+ img = img.convert(self.image_mode)
+ mat_path = os.path.join(self.data_dir, self.y_train[index] + self.annot_ext)
+
+ # Crop the face
+ pt2d = utils.get_pt2d_from_mat(mat_path)
+ x_min = min(pt2d[0,:])
+ y_min = min(pt2d[1,:])
+ x_max = max(pt2d[0,:])
+ y_max = max(pt2d[1,:])
+
+ # k = 0.2 to 0.40
+ k = np.random.random_sample() * 0.2 + 0.2
+ x_min -= 0.6 * k * abs(x_max - x_min)
+ y_min -= 2 * k * abs(y_max - y_min)
+ x_max += 0.6 * k * abs(x_max - x_min)
+ y_max += 0.6 * k * abs(y_max - y_min)
+ img = img.crop((int(x_min), int(y_min), int(x_max), int(y_max)))
+
+ # We get the pose in radians
+ pose = utils.get_ypr_from_mat(mat_path)
+ # And convert to degrees.
+ pitch = pose[0] * 180 / np.pi
+ yaw = pose[1] * 180 / np.pi
+ roll = pose[2] * 180 / np.pi
+
+ rnd = np.random.random_sample()
+ if rnd < 0.5:
+ ds = 10
+ original_size = img.size
+ img = img.resize((img.size[0] / ds, img.size[1] / ds), resample=Image.NEAREST)
+ img = img.resize((original_size[0], original_size[1]), resample=Image.NEAREST)
+
+ # Flip?
+ rnd = np.random.random_sample()
+ if rnd < 0.5:
+ yaw = -yaw
+ roll = -roll
+ img = img.transpose(Image.FLIP_LEFT_RIGHT)
+
+ # Blur?
+ rnd = np.random.random_sample()
+ if rnd < 0.05:
+ img = img.filter(ImageFilter.BLUR)
+
+ img_ycc = img.convert('YCbCr')
+
+ # Bin values
+ bins = np.array(range(-99, 102, 3))
+ binned_pose = np.digitize([yaw, pitch, roll], bins) - 1
+
+ labels = torch.LongTensor(np.concatenate((binned_pose, shape), axis = 0))
+ cont_labels = torch.FloatTensor([yaw, pitch, roll])
+
+ # Transforms
+ img = transforms.Scale(240)(img)
+ img = transforms.RandomCrop(224)(img)
+ img_ycc = img.convert('YCbCr')
+ img = transforms.ToTensor()
+ img_ycc = transforms.ToTensor()
+
+ return img, img_ycc, labels, cont_labels, self.X_train[index]
def __len__(self):
# 122,450
@@ -90,16 +269,17 @@
# Crop the face
pt2d = utils.get_pt2d_from_mat(mat_path)
+
x_min = min(pt2d[0,:])
y_min = min(pt2d[1,:])
x_max = max(pt2d[0,:])
y_max = max(pt2d[1,:])
- k = 0.15
- x_min -= k * abs(x_max - x_min)
- y_min -= 4 * k * abs(y_max - y_min)
- x_max += k * abs(x_max - x_min)
- y_max += 0.4 * k * abs(y_max - y_min)
+ k = 0.20
+ x_min -= 2 * k * abs(x_max - x_min)
+ y_min -= 2 * k * abs(y_max - y_min)
+ x_max += 2 * k * abs(x_max - x_min)
+ y_max += 0.6 * k * abs(y_max - y_min)
img = img.crop((int(x_min), int(y_min), int(x_max), int(y_max)))
# We get the pose in radians
@@ -111,14 +291,201 @@
# Bin values
bins = np.array(range(-99, 102, 3))
labels = torch.LongTensor(np.digitize([yaw, pitch, roll], bins) - 1)
+ cont_labels = torch.FloatTensor([yaw, pitch, roll])
if self.transform is not None:
img = self.transform(img)
- return img, labels, self.X_train[index]
+ return img, labels, cont_labels, self.X_train[index]
def __len__(self):
# 2,000
+ return self.length
+
+class AFLW2000_ds(Dataset):
+ def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.mat', image_mode='RGB'):
+ self.data_dir = data_dir
+ self.transform = transform
+ self.img_ext = img_ext
+ self.annot_ext = annot_ext
+
+ filename_list = get_list_from_filenames(filename_path)
+
+ self.X_train = filename_list
+ self.y_train = filename_list
+ self.image_mode = image_mode
+ self.length = len(filename_list)
+
+ def __getitem__(self, index):
+ img = Image.open(os.path.join(self.data_dir, self.X_train[index] + self.img_ext))
+ img = img.convert(self.image_mode)
+ mat_path = os.path.join(self.data_dir, self.y_train[index] + self.annot_ext)
+
+ # Crop the face
+ pt2d = utils.get_pt2d_from_mat(mat_path)
+ x_min = min(pt2d[0,:])
+ y_min = min(pt2d[1,:])
+ x_max = max(pt2d[0,:])
+ y_max = max(pt2d[1,:])
+
+ k = 0.20
+ x_min -= 2 * k * abs(x_max - x_min)
+ y_min -= 2 * k * abs(y_max - y_min)
+ x_max += 2 * k * abs(x_max - x_min)
+ y_max += 0.6 * k * abs(y_max - y_min)
+ img = img.crop((int(x_min), int(y_min), int(x_max), int(y_max)))
+
+ ds = 8
+ original_size = img.size
+ img = img.resize((img.size[0] / ds, img.size[1] / ds), resample=Image.NEAREST)
+ img = img.resize((original_size[0], original_size[1]), resample=Image.NEAREST)
+
+ # We get the pose in radians
+ pose = utils.get_ypr_from_mat(mat_path)
+ # And convert to degrees.
+ pitch = pose[0] * 180 / np.pi
+ yaw = pose[1] * 180 / np.pi
+ roll = pose[2] * 180 / np.pi
+ # Bin values
+ bins = np.array(range(-99, 102, 3))
+ labels = torch.LongTensor(np.digitize([yaw, pitch, roll], bins) - 1)
+ cont_labels = torch.FloatTensor([yaw, pitch, roll])
+
+ if self.transform is not None:
+ img = self.transform(img)
+
+ return img, labels, cont_labels, self.X_train[index]
+
+ def __len__(self):
+ # 2,000
+ return self.length
+
+class AFLW2000_random_ds(Dataset):
+ def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.mat', image_mode='RGB'):
+ self.data_dir = data_dir
+ self.transform = transform
+ self.img_ext = img_ext
+ self.annot_ext = annot_ext
+
+ filename_list = get_list_from_filenames(filename_path)
+
+ self.X_train = filename_list
+ self.y_train = filename_list
+ self.image_mode = image_mode
+ self.length = len(filename_list)
+
+ def __getitem__(self, index):
+ img = Image.open(os.path.join(self.data_dir, self.X_train[index] + self.img_ext))
+ img = img.convert(self.image_mode)
+ mat_path = os.path.join(self.data_dir, self.y_train[index] + self.annot_ext)
+
+ # Crop the face
+ pt2d = utils.get_pt2d_from_mat(mat_path)
+ x_min = min(pt2d[0,:])
+ y_min = min(pt2d[1,:])
+ x_max = max(pt2d[0,:])
+ y_max = max(pt2d[1,:])
+
+ k = 0.20
+ x_min -= 2 * k * abs(x_max - x_min)
+ y_min -= 2 * k * abs(y_max - y_min)
+ x_max += 2 * k * abs(x_max - x_min)
+ y_max += 0.6 * k * abs(y_max - y_min)
+ img = img.crop((int(x_min), int(y_min), int(x_max), int(y_max)))
+
+ rnd = np.random.random_sample()
+ if rnd < 0.5:
+ ds = 10
+ original_size = img.size
+ img = img.resize((img.size[0] / ds, img.size[1] / ds), resample=Image.NEAREST)
+ img = img.resize((original_size[0], original_size[1]), resample=Image.NEAREST)
+
+ # We get the pose in radians
+ pose = utils.get_ypr_from_mat(mat_path)
+ # And convert to degrees.
+ pitch = pose[0] * 180 / np.pi
+ yaw = pose[1] * 180 / np.pi
+ roll = pose[2] * 180 / np.pi
+ # Bin values
+ bins = np.array(range(-99, 102, 3))
+ labels = torch.LongTensor(np.digitize([yaw, pitch, roll], bins) - 1)
+ cont_labels = torch.FloatTensor([yaw, pitch, roll])
+
+ if self.transform is not None:
+ img = self.transform(img)
+
+ return img, labels, cont_labels, self.X_train[index]
+
+ def __len__(self):
+ # 2,000
+ return self.length
+
+class AFLW_aug(Dataset):
+ def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.txt', image_mode='RGB'):
+ self.data_dir = data_dir
+ self.transform = transform
+ self.img_ext = img_ext
+ self.annot_ext = annot_ext
+
+ filename_list = get_list_from_filenames(filename_path)
+
+ self.X_train = filename_list
+ self.y_train = filename_list
+ self.image_mode = image_mode
+ self.length = len(filename_list)
+
+ def __getitem__(self, index):
+ img = Image.open(os.path.join(self.data_dir, self.X_train[index] + self.img_ext))
+ img = img.convert(self.image_mode)
+ txt_path = os.path.join(self.data_dir, self.y_train[index] + self.annot_ext)
+
+ # We get the pose in radians
+ annot = open(txt_path, 'r')
+ line = annot.readline().split(' ')
+ pose = [float(line[1]), float(line[2]), float(line[3])]
+ # And convert to degrees.
+ yaw = pose[0] * 180 / np.pi
+ pitch = pose[1] * 180 / np.pi
+ roll = pose[2] * 180 / np.pi
+ # Something weird with the roll in AFLW
+ roll *= -1
+
+ # Augment
+ # Flip?
+ rnd = np.random.random_sample()
+ if rnd < 0.5:
+ yaw = -yaw
+ roll = -roll
+ img = img.transpose(Image.FLIP_LEFT_RIGHT)
+
+ # Blur?
+ # rnd = np.random.random_sample()
+ # if rnd < 0.05:
+ # img = img.filter(ImageFilter.BLUR)
+ # if rnd < 0.025:
+ # img = img.filter(ImageFilter.BLUR)
+ #
+ # rnd = np.random.random_sample()
+ # if rnd < 0.05:
+ # nb = np.random.randint(1,5)
+ # img = img.rotate(-nb)
+ # elif rnd > 0.95:
+ # nb = np.random.randint(1,5)
+ # img = img.rotate(nb)
+
+ # Bin values
+ bins = np.array(range(-99, 102, 3))
+ labels = torch.LongTensor(np.digitize([yaw, pitch, roll], bins) - 1)
+ cont_labels = torch.FloatTensor([yaw, pitch, roll])
+
+ if self.transform is not None:
+ img = self.transform(img)
+
+ return img, labels, cont_labels, self.X_train[index]
+
+ def __len__(self):
+ # train: 18,863
+ # test: 1,966
return self.length
class AFLW(Dataset):
@@ -149,22 +516,154 @@
pitch = pose[1] * 180 / np.pi
roll = pose[2] * 180 / np.pi
# Something weird with the roll in AFLW
- if yaw < 0:
- roll *= -1
+ roll *= -1
# Bin values
bins = np.array(range(-99, 102, 3))
labels = torch.LongTensor(np.digitize([yaw, pitch, roll], bins) - 1)
+ cont_labels = torch.FloatTensor([yaw, pitch, roll])
if self.transform is not None:
img = self.transform(img)
- return img, labels, self.X_train[index]
+ return img, labels, cont_labels, self.X_train[index]
def __len__(self):
# train: 18,863
# test: 1,966
return self.length
+class AFW(Dataset):
+ def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.txt', image_mode='RGB'):
+ self.data_dir = data_dir
+ self.transform = transform
+ self.img_ext = img_ext
+ self.annot_ext = annot_ext
+
+ filename_list = get_list_from_filenames(filename_path)
+
+ self.X_train = filename_list
+ self.y_train = filename_list
+ self.image_mode = image_mode
+ self.length = len(filename_list)
+
+ def __getitem__(self, index):
+ txt_path = os.path.join(self.data_dir, self.y_train[index] + self.annot_ext)
+ img_name = self.X_train[index].split('_')[0]
+
+ img = Image.open(os.path.join(self.data_dir, img_name + self.img_ext))
+ img = img.convert(self.image_mode)
+ txt_path = os.path.join(self.data_dir, self.y_train[index] + self.annot_ext)
+
+ # We get the pose in degrees
+ annot = open(txt_path, 'r')
+ line = annot.readline().split(' ')
+ yaw, pitch, roll = [float(line[1]), float(line[2]), float(line[3])]
+
+ # Crop the face
+ k = 0.32
+ x1 = float(line[4])
+ y1 = float(line[5])
+ x2 = float(line[6])
+ y2 = float(line[7])
+ x1 -= 0.8 * k * abs(x2 - x1)
+ y1 -= 2 * k * abs(y2 - y1)
+ x2 += 0.8 * k * abs(x2 - x1)
+ y2 += 1 * k * abs(y2 - y1)
+
+ img = img.crop((int(x1), int(y1), int(x2), int(y2)))
+
+ # Bin values
+ bins = np.array(range(-99, 102, 3))
+ labels = torch.LongTensor(np.digitize([yaw, pitch, roll], bins) - 1)
+ cont_labels = torch.FloatTensor([yaw, pitch, roll])
+
+ if self.transform is not None:
+ img = self.transform(img)
+
+ return img, labels, cont_labels, self.X_train[index]
+
+ def __len__(self):
+ # Around 200
+ return self.length
+
+class BIWI(Dataset):
+ def __init__(self, data_dir, filename_path, transform, img_ext='.png', annot_ext='.txt', image_mode='RGB'):
+ self.data_dir = data_dir
+ self.transform = transform
+ self.img_ext = img_ext
+ self.annot_ext = annot_ext
+
+ filename_list = get_list_from_filenames(filename_path)
+
+ self.X_train = filename_list
+ self.y_train = filename_list
+ self.image_mode = image_mode
+ self.length = len(filename_list)
+
+ def __getitem__(self, index):
+ img = Image.open(os.path.join(self.data_dir, self.X_train[index] + '_rgb' + self.img_ext))
+ img = img.convert(self.image_mode)
+ pose_path = os.path.join(self.data_dir, self.y_train[index] + '_pose' + self.annot_ext)
+
+ y_train_list = self.y_train[index].split('/')
+ bbox_path = os.path.join(self.data_dir, y_train_list[0] + '/dockerface-' + y_train_list[-1] + '_rgb' + self.annot_ext)
+
+ # Load bounding box
+ bbox = open(bbox_path, 'r')
+ line = bbox.readline().split(' ')
+ if len(line) < 4:
+ x_min, y_min, x_max, y_max = 0, 0, img.size[0], img.size[1]
+ else:
+ x_min, y_min, x_max, y_max = [float(line[1]), float(line[2]), float(line[3]), float(line[4])]
+ bbox.close()
+
+ # Load pose in degrees
+ pose_annot = open(pose_path, 'r')
+ R = []
+ for line in pose_annot:
+ line = line.strip('\n').split(' ')
+ l = []
+ if line[0] != '':
+ for nb in line:
+ if nb == '':
+ continue
+ l.append(float(nb))
+ R.append(l)
+
+ R = np.array(R)
+ T = R[3,:]
+ R = R[:3,:]
+ pose_annot.close()
+
+ roll = np.arctan2(R[1][0], R[0][0]) * 180 / np.pi
+ yaw = np.arctan2(-R[2][0], np.sqrt(R[2][1] ** 2 + R[2][2] ** 2)) * 180 / np.pi
+ pitch = -np.arctan2(R[2][1], R[2][2]) * 180 / np.pi
+
+ # Loosely crop face
+ k = 0.35
+ x_min -= 0.6 * k * abs(x_max - x_min)
+ y_min -= k * abs(y_max - y_min)
+ x_max += 0.6 * k * abs(x_max - x_min)
+ y_max += 0.6 * k * abs(y_max - y_min)
+ img = img.crop((int(x_min), int(y_min), int(x_max), int(y_max)))
+
+ # Bin values
+ bins = np.array(range(-99, 102, 3))
+ binned_pose = np.digitize([yaw, pitch, roll], bins) - 1
+
+ labels = torch.LongTensor(binned_pose)
+ cont_labels = torch.FloatTensor([yaw, pitch, roll])
+
+ if self.transform is not None:
+ img = self.transform(img)
+
+ return img, labels, cont_labels, self.X_train[index]
+
+ def __len__(self):
+ # 15,667
+ return self.length
+
+
def get_list_from_filenames(file_path):
# input: relative path to .txt file with file names
# output: list of relative path names
--
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